Masked Face Recognition for Secure Contactless Systems: A Deep Learning Framework

How difficult is it to assume large-scale usage of face masks for the infection prevention within a traditional facial recognition system, as people with masks blur facial features considerably, which becomes harder to differentiate? We seek to develop an advanced masked face recognition system using high-end deep learning and cutting-edge image processing techniques in this regard. The purpose is to make identification possible even when people wear masks. Consequently, it is an essential capability in security checkpoints, access control systems, and public surveillance where good recognition is required. We shall feed our machine learning models with the abundant masked face datasets recently found in facial recognition research so that they can recognise visible features despite their partial coverage. This should lead to a system that can be deployed robustly across most face coverings.The successful implementation of masked face recognition will bring benefits to sectors needing high security and efficiency. Advanced identification capabilities will enhance safety at check points and surveillance due to the enhancement of threat detection as well as a flow within monitoring. This system, therefore, would allow contactless identification in healthcare. The result is a minimisation of the risk of infection-a very important aspect during the pandemics. Public spaces such as malls or high-traffic airports could use this technology to check identities rapidly, reducing wait times while satisfying the need for security. For a long time, mask-wearing has continued to being the norm in many places from the COVID-19 to the latest HMPV, making this project contribute to safer, as well as more efficient, recognition in various public and private settings.